Alright, so today I’m gonna walk you through my little experiment: New York vs. Toronto. I know, it sounds kinda random, but bear with me.
The Setup:
- Basically, I was bored and had some free cloud credits kicking around. I figured, why not spin up two identical servers, one in New York and one in Toronto, and see how they perform?
- I chose a pretty basic setup: Ubuntu 22.04, same server specs (nothing fancy, just a couple of vCPUs and some RAM), and the same software stack. We’re talking Nginx, a simple Python Flask app, and a PostgreSQL database. Standard stuff.
Getting Started:
First, I fired up the servers. Cloud provider made it super easy, click, click, done. Then, I installed all the dependencies. Used the same Ansible playbook for both servers to make sure everything was identical. This part took a bit longer than expected. Ran into a dependency issue with one of the Python packages, but a quick Google search and a `pip install –upgrade` fixed it right up.
Deploying the App:
Next, I deployed my super simple Flask app. It’s basically just an API endpoint that queries the PostgreSQL database and returns some JSON data. Nothing groundbreaking, but enough to put the servers through their paces. Used `git clone` to pull the code, set up a virtual environment, installed the requirements, and then configured Nginx to proxy requests to the app.
Database Time:
Okay, PostgreSQL was next. Installed it, created a user, created a database, and then populated it with some dummy data. I used a script to insert a few thousand rows of randomly generated data. Again, nothing too complicated, just enough to simulate a real-world scenario.
The Testing:
This is where it got interesting. I used `ab` (ApacheBench) to send a bunch of requests to both servers and measured the response times, requests per second, and error rates. I ran several tests with different concurrency levels (e.g., 10 concurrent users, 50 concurrent users, 100 concurrent users) to see how the servers handled the load.
The Results:
Honestly? The results were pretty similar. Both servers performed well, with no major differences in response times or error rates. Toronto was maybe slightly faster in some tests, but it was within the margin of error. I’m talking milliseconds here, nothing you’d notice in the real world.
What I Learned:
So, what did I learn? Well, for this particular setup, there’s not a huge difference between New York and Toronto. Location matters, sure, but with modern infrastructure, the differences are often negligible for basic workloads. It really depends on the specific application and the network topology. Also, I relearned that even simple experiments can take way longer than you think. Debugging those Python dependencies took a good chunk of my afternoon.
Wrapping Up:
Anyway, that’s my little New York vs. Toronto adventure. It wasn’t earth-shattering, but it was a fun way to kill some time and learn a thing or two. Maybe next time I’ll try comparing different cloud providers or different server configurations. Stay tuned!
